scholarly journals Housing Market in the Time of Pandemic: A Price Gradient Analysis from the COVID-19 Epicentre in China

2021 ◽  
Vol 14 (3) ◽  
pp. 108
Author(s):  
Ka Shing Cheung ◽  
Chung Yim Yiu ◽  
Chuyi Xiong
Keyword(s):  
Gradient Analysis ◽  
House Prices ◽  
Urban Housing ◽  
Hedonic Pricing ◽  
Real Estate Market ◽  
Asset Markets ◽  
Residential Areas ◽  
The Real Estate ◽  
The Real Estate Market ◽  
Gradient Models

While the outbreak of the COVID-19 disease has caused asset markets to experience an unprecedented spike of risk and uncertainty worldwide, the real estate market in many global cities appears to be immune to the adverse effects. How does COVID-19 affect urban housing markets? This study is a first attempt to identify the pandemic’s impact on house prices by applying a price gradient analysis to the COVID-19 epicentre in China. Considering microlevel housing transaction data in 62 areas from nine districts in Wuhan City from January 2019 to July 2020, the hedonic pricing and the price gradient models suggest that there was, respectively, a 4.8% and a 5.0–7.0% year-on-year fall in house prices immediately after the pandemic outbreak. Although house prices rebounded after the lockdown period, the gradient models show that the price gradients were flattened from the epicentre to the urban peripherals. The price premiums in high-density areas were also substantially discounted after the city’s lockdown. Our findings are robust to different model specifications. The implication is that the risk associated with the pandemic is localised and transitory in nature. People may be able to internalise the risk by residing in low-density residential areas.

An Institutional Countermeasure to China's High Urban Housing Prices

2013 ◽  
Vol 448-453 ◽  
pp. 4075-4078
Author(s):  
Jin Zhang
Keyword(s):  
Civil Rights ◽  
Real Estate ◽  
Institutional Economics ◽  
Housing Prices ◽  
Urban Housing ◽  
Real Estate Market ◽  
System Level ◽  
The Real Estate ◽  
Economic Phenomenon ◽  
The Real Estate Market

The economic phenomenon of high urban housing prices in our country reflects asymmetry of rights and interests among government, real estate developers and buyers in essence, and behind this economic phenomenon imbedded financial crisis as well as political and social crisis. Regarding academic thought on the causes of high housing prices in the real estate market such as the theories of supply anddemand, cost, the system, and power imbalance between interest groups, this thesis proceeds institutional analysis, from the perspective of institutional economics, discusses the institutional causes of the persistent existence of four factors theories in the angle of vacancy of civil rights in the supply process of institution and rules, and puts forward policy suggestions of increasing effective supplies of institution in the system level.

New Mortgage Lenders and the Housing Market*

2020 ◽  
Author(s):  
Nikodem Szumilo
Keyword(s):  
Risk Taking ◽  
Financial Stability ◽  
House Prices ◽  
Housing Prices ◽  
Real Estate Market ◽  
Mortgage Lending ◽  
Mortgage Market ◽  
The Real Estate ◽  
Banking Market ◽  
The Real Estate Market

Abstract This article examines the effect of a new lender’s entry into a local mortgage market on the supply of new loans, housing prices and repossessions in areas around its branches. I use the decision of the European Commission to force the UK’s largest retail bank to divest a part of its business as a shock to the entry of a new lender, and show that incumbent banks increase mortgage lending in areas where the new bank has its branches. Furthermore, house prices increase by around 5% in the real estate market impacted by the shock. Average transaction numbers and mortgage repossession rates also increase in places where the new bank enters. Overall, my results show that increased competition in the banking market can have adverse consequences for risk-taking and financial stability.

scholarly journals The Influence of Home Buyer Sentiment on Chinese Housing Prices—— Based on Media Text Mining

2018 ◽  
Vol 10 (9) ◽  
pp. 145
Author(s):  
Qing Gao ◽  
Tianxiao Zhao
Keyword(s):  
Text Mining ◽  
House Prices ◽  
Housing Prices ◽  
Real Estate Market ◽  
Factors Affecting ◽  
The Real Estate ◽  
The Real Estate Market ◽  
Long Time ◽  
Confidence Index ◽  
Home Buyer

In recent years, housing prices in major cities in China have generally maintained an upward trend, and housing prices have remained high for a long time. China’s economic growth has its own particularity in the world, and the real estate market as a major support point for the economy, its future development is crucial to the economy. Taking Guangzhou as an example, this paper uses text mining method to extract media influence from media texts, and constructs the buyer confidence index, and uses it as a proxy variable for buyers’ mental expectations. This article finally obtained a total of 115,139 articles on the housing market, forming an article pool for text mining. In the study and analysis of the explanatory effect of the home buyer confidence index on housing prices, this paper uses multiple linear regression to analyze the main factors affecting housing prices, and concludes that buyers’ sentiment is significantly positively correlated with house prices.

scholarly journals Real estate boom in Chile and fundamentals on house prices

2020 ◽  
Vol 6 (1) ◽  
pp. 1-26
Author(s):  
C. Aguilera Alvial
Keyword(s):  
Real Estate ◽  
Interest Rates ◽  
Structural Breaks ◽  
House Prices ◽  
Housing Prices ◽  
Real Estate Market ◽  
The Real ◽  
The Real Estate ◽  
Integration Test ◽  
The Real Estate Market

This article studies the fundamentals of housing prices based on the Real Index of Housing Prices (IRPV), given that in recent times in Chile there has been a sustained increase in price levels and seeks to find evidence on the existence of a possible speculative bubble in the real estate market. Following the methodology of various Chilean and international authors, the Engle & Granger Co-integration methodology was applied. Furthermore, the results of the previous methodology were compared using the Johansen Co-integration test. Then a method to find structural breaks is applied. As a result, evidence is found to not reject the existence of a bubble in the real estate market. It is found that only interest rates co-integrate in the long term with the evolution of house prices, while the other fundamentals present a spurious relationship.

scholarly journals Spatio-Temporal Analysis of the Real Estate Market Using Geographic Information Systems

2013 ◽  
Vol 21 (2) ◽  
pp. 72-82 ◽  
Author(s):  
Piotr Cichociński ◽  
Janusz Dąbrowski
Keyword(s):  
Spatial Distribution ◽  
Real Estate ◽  
House Prices ◽  
Temporal Analysis ◽  
Real Estate Market ◽  
Geographic Information ◽  
Numerical Comparison ◽  
The Real ◽  
The Real Estate ◽  
The Real Estate Market

Abstract The paper proposes the use of geographic information system tools for the analysis of spatial and temporal aspects of the real estate market. In particular, it focuses on the graphical presentation of the spatial distribution of price and its variability over time. The possibility of presenting an image of the spatial distribution of prices in the form of a 3D model is studied. A topographic surface is proposed as an alternative to traditional methods of spatial interpolation. Visual verification and numerical comparison have shown its superiority over other previously used methods. The best method of presenting four-dimensional data - the variation in time of the spatial distribution of house prices - was sought. The possibility of taking time into account as one of the attributes of the analyzed and presented objects, available in advanced GIS software, was used for this purpose. The undertaken activities were based on formal guidelines for the registration of time set out in the ISO 19100 series of standards dedicated to geographic information. Potential sources of data for this kind of analysis were identified and their availability was examined. The paper also presents how to build a spatial database on the basis of the available information, which is a starting material for further analysis. The carried out research demonstrated the benefits of the spatial approach to trends of changes in real estate prices, which can be used, among others, for mass appraisal.

Reducing Vacant Houses Is More Important than the Implementation of Green Buildings

2011 ◽  
Vol 280 ◽  
pp. 250-254 ◽  
Author(s):  
Wang Hu Sun ◽  
Shu Chen Xu ◽  
Li Sha Li
Keyword(s):  
Real Estate ◽  
House Prices ◽  
Supply And Demand ◽  
Green Building ◽  
Real Estate Market ◽  
Short Supply ◽  
The Real ◽  
The Real Estate ◽  
The Real Estate Market ◽  
Vacant Housing

The purpose of this paper sets forth the various hazards of vacant rooms. By analyzing the phenomena of the Housing and relevant data, the paper demonstrates the urban and town housing in China is not in short supply in the aggregate and the discrepancy between housing supply and demand is due to the real estate market speculation and investment which have led to the massive housing vacancy. These vacant houses waste a lot of resources, impact the life of other inhabitants, impede the developing process of the city’s livability, distort the relationship between the supply and demand in the real estate market, and push up the house prices. Therefore, the reduction of vacant housing is more important than the implementation of green building, this conclusion, and vacant housing hazards and data are where the innovation of this article.

scholarly journals HEDONIC PRICING AT NOVOSIBIRSK HOUSING MODERN MARKET

2020 ◽  
Vol 3 (1) ◽  
pp. 75-80
Author(s):  
Yuri Yu. Kazantsev
Keyword(s):  
Housing Stock ◽  
Hedonic Pricing ◽  
Real Estate Market ◽  
Similar Data ◽  
The Real Estate ◽  
The Real Estate Market ◽  
Key Factor ◽  
Residential Properties ◽  
Price Differences ◽  
The City

The paper is aimed at searching a hedonic margin on housing in the city of Novosibirsk. Hedonic pricing confirms the price difference of residential properties in the context of a large list of matches. The methodology that has been proven in foreign studies is used to identify the causes of price differences, however, in this study we observe a traditional Soviet housing stock that is not competitive in modern urban planning. Usually the hedonic margin refers to the environmental or recreational characteristics of the area; thisstudy is more a tendency to the second option. It seems to be a novelty to reduce the complex of parameters of hedonic pricing to one key factor. The main problems of mathematical modeling of the hedonic behavior of the buyer in the real estate market in Novosibirsk are identified. The results are comparable with similar data from foreign colleagues.

scholarly journals Clustering Analysis with Embedding Vectors: An Application to Real Estate Market Delineation

2021 ◽  
Vol 7 (1) ◽  
pp. 30-40
Author(s):  
Changro Lee
Keyword(s):  
Real Estate ◽  
Clustering Analysis ◽  
Clustering Algorithm ◽  
Hedonic Pricing ◽  
Real Estate Market ◽  
Categorical Variables ◽  
The Real Estate ◽  
The Real Estate Market ◽  
Gyeonggi Province ◽  
Vector Representations

Although clustering analysis is a popular tool in unsupervised learning, it is inefficient for the datasets dominated by categorical variables, e.g., real estate datasets. To apply clustering analysis to real estate datasets, this study proposes an entity embedding approach that transforms categorical variables into vector representations. Three variants of a clustering algorithm, i.e., the clustering based on the traditional Euclidean distance, the Gower distance, and the embedding vectors, are applied to the land sales records to delineate the real estate market in Gwacheon-si, Gyeonggi province, South Korea. Then, the relevance of the resultant submarkets is evaluated using the root mean squared errors (RMSE) obtained from a hedonic pricing model. The results show that the RMSE in the embedding vector-based algorithm decreases substantially from 0.076-0.077 to 0.069. This study shows that the clustering algorithm empowered by embedding vectors outperforms the conventional algorithms, thereby enhancing the relevance of the delineated submarkets.

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